Each year this research examines the analytic behaviors, needs, preferences, andviews of data mining professionals. It is conducted as a service to the data miningcommunity. It s not conducted for, or sponsored by, any third party. Rexer Analyticsis committed to freely disseminating our research findings through reportsummaries, conference presentations, and personal contact. If you would like acopy of this year's FREE 37 page summary report, or summary reports fromprevious years, please contact us at DataMinerSurvey@RexerAnalytics.com. Please also contact us if you have questions about this research program or if youhave suggestions for topics that should be included in future data miner surveys.

Rexer Analytics has been conducting the Data Miner Survey since 2007. Summary reports (PDFs of about 40 pages) of each of the six surveys are available FREE to everyone -- simply email your request to DataMinerSurvey@RexerAnalytics.com. Also, highlights of each Data Miner Survey are available online, including bestpractices shared by respondents on analytic success measurement, overcomingdata mining challenges, and other topics.

FIELDS & GOALS: Data miners work in a diverse set of fields. CRM / Marketing has been the #1 field in each of the past four years. Fittingly, “improving the understanding of customers”, “retaining customers” and other CRM goals are also the goals identified by the most data miners surveyed.

ALGORITHMS: Decision trees, regression, and cluster analysis continue to form a triad of core algorithms for most data miners. However, a wide variety of algorithms are being used. This year, for the first time, the survey asked about Ensemble Models, and 22% of data miners report using them.

MODELS: About one-third of data miners typically build final models with 10 or fewer variables, while about 28% generally construct models with more than 45 variables.

TOOLS: After a steady rise across the past few years, the open source data mining software R overtook other tools to become the tool used by more data miners (43%) than any other. STATISTICA, which has also been climbing in the rankings, is selected as the primary data mining tool by the most data miners (18%). Data miners report using an average of 4.6 software tools overall. STATISTICA, IBM SPSS Modeler, and R received the strongest satisfaction ratings in both 2010 and 2009.

TECHNOLOGY: Data Mining most often occurs on a desktop or laptop computer, and frequently the data is stored locally. Model scoring typically happens using the same software used to develop models. STATISTICA users are more likely than other tool users to deploy models using PMML.

CHALLENGES: As in previous years, dirty data, explaining data mining to others, and difficult access to data are the top challenges data miners face. This year data miners also shared best practices for overcoming these challenges.Read about their experiences overcoming these challenges.

FUTURE: Data miners are optimistic about continued growth in the number of projects they will be conducting, and growth in data mining adoption is the number one “future trend” identified. There is room to improve: only 13% of data miners rate their company’s analytic capabilities as “excellent” and only 8% rate their data quality as “very strong”.